内插模型的精度评价问题一直是DEM内插研究中的热点问题。以往较多的研究关注插值模型本身的精度评价,却忽略了插值模型与应用环境之间的交互作用,例如,普通克里金方法作DEM内插一般精度较差,但是当插值区域平坦时,该方法的插值精度却很高,这表明该方法对平坦地形的插值问题具有较好的适应性。为了分析不同插值模型在不同地形环境下的适用性,本文选取陕北黄土高原地区不同地貌类型的实验样区,应用AMMI模型对不同内插模型的精度,以及对不同地貌类型的适用性进行评价,该模型最大的特点是很好地结合了方差分析与回归分析的特点,特别适合于不同影响因素之间交互作用的分析。实验结果表明,AMMI模型可以有效地分析内插方法与地貌环境对内插精度的交互作用,不同的内插方法对不同的地貌类型区的适用性存在差异。以本文的研究为例,在陕北黄土高原地区最稳定的DEM内插方法是样条函数法,而反距离加权法与Top to Raster方法精度会更高。最后,通过对环境指数与若干地形因子的相关性分析,表明地貌类型区的坡度可以粗略地代表第一环境指数。
In China′s Loess Plateau area, gully head is the most active zone of a drainage system in gully areas. The differentiation of loess gully head follows geospatial patterns and reflects the process of the loess landform development and evolution of its drainage system to some extent. In this study, the geomorphic meaning, basic characteristics, morphological structure and the basic types of loess gully heads were systematically analysed. Then, the loess gully head′s conceptual model was established, and an extraction method based on Digital Elevation Model(DEM) for loess gully head features and elements was proposed. Through analysing the achieved statistics of loess gully head features, loess gully heads have apparently similar and different characteristics depending on the different loess landforms where they are found. The loess head characteristics reflect their growth period and evolution tendency to a certain degree, and they indirectly represent evolutionary mechanisms. In addition, the loess gully developmental stages and the evolutionary processes can be deduced by using loess gully head characteristics. This study is of great significance for development and improvement of the theoretical system for describing loess gully landforms.
The spatial structure characteristics of landform are the foundation of geomorphologic classification and recognition.This paper proposed a new method on quantifying spatial structure characteristics of terrain surface based on improved 3D Lacunarity model.Lacunarity curve and its numerical integration are used in this model to improve traditional classification result that different morphological types may share the close value of indexes based on global statistical analysis.Experiments at four test areas with different landform types show that improved 3D Lacunarity model can effectively distinguish different morphological types per texture analysis.Higher sensitivity in distinguishing the tiny differences of texture characteristics of terrain surface shows that the quantification method by 3D Lacu-narity model and its numerical integration presented in this paper could contribute to improving the accuracy of land-form classifications and relative studies.
